Creating a data governance framework is a must-have for organizations that want to be truly data-driven. A data governance framework provides the essential structure that’s needed for the core elements of data management—data privacy and data security.

Despite the importance of prioritizing data governance, it is a difficult project for many organizations. A data governance framework eliminates the challenges by setting a clear path for building and maintaining a data governance program. It guides everything from identifying requirements and developing a plan to establishing a team and implementing the plan.  

Done well, a data governance framework enables organizations to make the implementation of a data governance plan a smooth process that does not necessarily require a complete overhaul of existing data management systems. It helps organizations align and catalog existing data assets and establishes processes for ensuring that new data is organized and stored according to the data governance guidelines.

Let’s jump in and learn:

Data governance is the practice of identifying, organizing, and managing data across an organization. It provides standards and procedures to keep data readily available and usable with data protection to ensure data privacy and data integrity. It is the decision-making function that overlays data management. 

The collection of practices and processes that define the use of data are a key part of developing a data governance framework. By defining how data will be used and managed, a data governance framework helps organizations enforce rules that dictate data access and usage as well as provides direction on storage and retention. This is how data management assures optimal availability, usability, integrity, and security of the data across an organization.

A data governance framework directs how data will be managed and controlled. Among the areas commonly included in data governance frameworks are data policies, such as:

Accessible and ready for easy consumption.  

Data qualities are retained, across different platforms, regardless of how it is stored, converted, transferred, and viewed.

Ensures data is correct, consistent, and cleaned.   

Safeguards are in place, including classification according to its sensitivity and storage policies and procedures that ensure data protection.    

Clearly structured, documented, and tagged.

An actionable data governance framework includes rules, processes, and procedures to be followed on an ongoing basis. The primary objective is to enforce and facilitate data governance. In addition, a data governance framework details the components needed to implement and maintain a governance strategy.

A data governance framework should also include a statement of purpose, goals, and details for measuring success. It should spell out responsibilities and accountability for the various functions responsible for achieving the organization’s goals. All of that should be documented as part of the data governance framework and shared internally to ensure that everyone understands it and is committed to supporting it.

The structure of a data governance framework is meant to help organizations establish roles and responsibilities related to data, guide decision-making, and facilitate the use of data to maintain data quality and ensure data protection. Depending on the requirements, different types of data governance frameworks can be used. There are three types of frameworks:

1. Command and control—a small group of data stewards are assigned to take on data governance management responsibilities

2. Traditional—a larger number of employees are assigned as data stewards, with a few having additional responsibilities as critical data stewards

3. Non-invasive—individuals act as data stewards according to their existing work and relationship to the data (i.e., anyone who creates and modifies data becomes a data steward for that data)

A successful data governance framework has many components, including:

Including stakeholders in data governance framework development is crucial. These stakeholders should represent the different areas and functions within an organization. Stakeholders vary based on the structure of the organization and use cases, but generally fall into a few categories:

It is important to establish the goals of a data governance framework before beginning to develop it. In detailing the goals, all constituents should be consulted to ensure that their needs will be met. Common goals of a data governance framework include the following.

A data governance framework can help organizations clearly understand the types of data they have as well as the value of it and its level of sensitivity (e.g., personally identifiable information or PII, protected health information or PHI). This makes it easier not just to find information, but also to employ sufficient data protection. In addition, by helping to classify information, data governance frameworks simplify the management of data lifecycles (i.e., data creation, data storage, data use, data archiving, and data destruction).

Data security plays a significant role in a data governance framework. Enhancing data protection is a key goal of most data governance framework projects. A data governance framework will cover a range of data protection and data privacy areas, including software and hardware protection, data access, storage, and authentication processes.

Data governance frameworks help organizations assess and maintain data quality.

A data governance framework includes processes for determining when good data becomes obsolete and should be destroyed.

Data governance frameworks dictate the level of access and accountability that users have with content and ensure that the same data can be shared among multiple users and applications while maintaining data quality and data integrity.

One of the primary drivers behind data governance framework projects is to address compliance requirements, such as Sarbanes-Oxley Act, General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and The Payment Card Industry Data Security Standard (PCI-DSS).

A data governance framework is crucial to getting the most out of data governance programs. It provides standards for organizations’ rules and establishes processes for storing, using, and collecting data. The data governance framework identifies data owners, creates catalogs, improves data availability, increases data literacy and access, and sets procedures to enforce data policies.

In addition, a data governance framework sets up data risk and privacy metrics, including key performance indicators (KPIs), to gain insight into the progress of adoption and workflows. It also provides details about how data is being used and who uses it, using technical metadata, data profiling and lineage, and collaboration tracking.

Putting a data governance framework in place gives organizations a detailed plan with processes to make data accessible, protect it, and ensure data quality. A data governance framework supports organizations in several other ways, including:

There are four pillars to the data governance framework to enable organizations to get the most out of their data.

1. Identify distinct use cases

It is of the utmost importance to connect data governance to its impact on an organization by considering revenue, cost, and risk. Identifying use cases provides an opportunity to engage stakeholders and make them part of developing the data governance framework. 

Getting stakeholders involved not only helps ensure that real, meaningful issues are being addressed, but also gets them invested in the project and committed to its success. Examples of use cases include:

The number of use cases for data governance is practically limitless. But the data governance framework development works best by starting with a solid use case that represents characteristics of most of the others. Then, take lessons learned from the initial use case to deploy the data governance framework across the organization. 

2. Quantify value

It is important that the impact of the data governance implementation is measurable. The value of the data governance framework must be quantifiable in the individual use cases and for the organization as a whole. 

Key performance indicators (KPIs) need to be established and reported out regularly to show the value delivered from the data governance framework. These will also help identify areas that require attention and improvement.

3. Improve data capabilities

The data governance framework should outline the capabilities that are needed to improve the value of data for users and address individual needs for data usage. This should include the technology and processes needed to empower users to make the most of available data to meet requirements that are set forth in the use cases. Capabilities that are commonly included are the ability to:

4. Develop a scalable delivery model

After the initial use case is addressed, the data governance framework can be established as a scalable service. The effort should decrease with each new use case while delivering increased benefits. In this way, the data governance framework grows the return on investment as more use cases are added and, ultimately, it is applied across an organization. 

Mind the four pillars for success when dealing with a data governance framework, and remember that it is a strategic asset that should be developed, maintained, and protected with care on an ongoing basis. It should be treated as a practice that is embedded in all of an organization’s workflows.   

Considerations and steps to take when creating a data governance framework include the following.  

Perhaps most important is maintenance. A data governance framework should be a living process that grows and changes with organizations’ needs. Put processes in place to ensure that the data governance framework is regularly reviewed by all stakeholders and updated as needed.

Egnyte has experts ready to answer your questions. For more than a decade, Egnyte has helped more than 16,000 customers with millions of customers worldwide.

Last Updated: 9th March, 2022

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